Real-time Optimal Resource Allocation for Embedded UAV Communication Systems

We consider device-to-device (D2D) wireless information and power transfer systems using an unmanned aerial vehicle (UAV) as a relay-assisted node. As the energy capacity and flight time of UAVs is limited, a significant issue in deploying UAV is to manage energy consumption in real-time application, which is proportional to the UAV transmit power. To tackle this important issue, we develop a real-time resource allocation algorithm for maximizing the energy efficiency by jointly optimizing the energy-harvesting time and power control for the considered (D2D) communication embedded with UAV. We demonstrate the effectiveness of the proposed algorithms as running time for solving them can be conducted in milliseconds.Comment: 11 pages, 5 figures, 1 table. This paper is accepted for publication on IEEE Wireless Communications Letter

Sound-Dr: Reliable Sound Dataset and Baseline Artificial Intelligence System for Respiratory Illnesses

As the burden of respiratory diseases continues to fall on society worldwide, this paper proposes a high-quality and reliable dataset of human sounds for studying respiratory illnesses, including pneumonia and COVID-19. It consists of coughing, mouth breathing, and nose breathing sounds together with metadata on related clinical characteristics. We also develop a proof-of-concept system for establishing baselines and benchmarking against multiple datasets, such as Coswara and COUGHVID. Our comprehensive experiments show that the Sound-Dr dataset has richer features, better performance, and is more robust to dataset shifts in various machine learning tasks. It is promising for a wide range of real-time applications on mobile devices. The proposed dataset and system will serve as practical tools to support healthcare professionals in diagnosing respiratory disorders. The dataset and code are publicly available here: https://github.com/ReML-AI/Sound-Dr/.Comment: 9 pages, PHMAP2023, PH

Reanalysis of the settlement of a levee on soft bay mud

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, February 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 261-264).Staged construction of embankments on soft ground remains one of the most challenging topics in geotechnical engineering due to the complex shear and consolidation behavior of clays. This thesis presents a case study on the performance of the New Hamilton Partnership (NHP) levee in Novato, California. This 11ft high levee was constructed over 30 ft - 40 ft thick layer of San Francisco Bay Mud during a six-month period in 1996. Settlements along the levee crest were monitored over a period of 5.2 years after the end of construction (until early 2002), at which time URS installed piezometers to measure the existing consolidation stresses (s'vc) within the Bay Mud. URS also conducted state-of-the art field and laboratory test programs to develop well-defined values of preconsolidation stress (s'p) and compressibility parameters for the Bay Mud. However, conventional 1-D consolidation analyses greatly underestimated the measured levee settlements. Hence URS reduced s'p by 20% for the Plaxis FE analyses with the Soft Soil Model (SSM) used to replicate the performance of the existing NHP levee and then to design an expanded levee system. This thesis presents a detailed re-evaluation of the NHP levee performance and of the stress history, strength, and consolidation properties of the Bay Mud obtained during the URS geotechnical site investigation.(cont.) New conventional 1-D consolidation analyses with higher values of the recompression ratio and revised profiles of s'vc indicate that the measured levee settlements at 5.2 years can be matched when s'p is reduced by 10% to 15%. The thesis also presents two series of Plaxis analyses with the Soft Soil Model. The first evaluated SSM parameters to better model results from the laboratory consolidation and K0-consolidated undrained shear tests on the Bay Mud. The second series conducted 2-D FE analyses to identify the most important variables effecting the predicted performance of the levee during and after construction. These parametric analyses show that the measured settlements during the 5.2 year period and the excess pore pressures measured in early 2002 can be consistently described only after careful definition of four major variables: the recompression ratio, RR, the normally consolidated coefficient of consolidation, cv(NC), and the preconsolidation stress, s'p, of the Bay Mud; and the boundary drainage conditions. The measured performance is best matched by using values of cv(NC) and s'p that are less than measured by the laboratory CRSC tests.(cont.) Analyses with more sophisticated soil models are needed before definitive conclusions can be reached regarding the in situ properties of the Bay Mud and whether no not secondary compression (creep) plays an important role during primary consolidation (i.e., Hypothesis A versus Hypothesis B).by Hoang Q. Nguyen.S.M

Joint Fractional Time Allocation and Beamforming for Downlink Multiuser MISO Systems

It is well-known that the traditional transmit beamforming at a base station (BS) to manage interference in serving multiple users is effective only when the number of users is less than the number of transmit antennas at the BS. Non-orthogonal multiple access (NOMA) can improve the throughput of users with poorer channel conditions by compromising their own privacy because other users with better channel conditions can decode the information of users in poorer channel state. NOMA still prefers that the number of users is less than the number of antennas at the BS transmitter. This paper resolves such issues by allocating separate fractional time slots for serving the users with similar channel conditions. This enables the BS to serve more users within the time unit while the privacy of each user is preserved. The fractional times and beamforming vectors are jointly optimized to maximize the system's throughput. An efficient path-following algorithm, which invokes a simple convex quadratic program at each iteration, is proposed for the solution of this challenging optimization problem. Numerical results confirm its versatility.Comment: IEEE Communications Letters (To Appear

An in-situ thermoelectric measurement apparatus inside a thermal-evaporator

At the ultra-thin limit below 20 nm, a film's electrical conductivity, thermal conductivity, or thermoelectricity depends heavily on its thickness. In most studies, each sample is fabricated one at a time, potentially leading to considerable uncertainty in later characterizations. We design and build an in-situ apparatus to measure thermoelectricity during their deposition inside a thermal evaporator. A temperature difference of up to 2 K is generated by a current passing through an on-chip resistor patterned using photolithography. The Seebeck voltage is measured on a Hall bar structure of a film deposited through a shadow mask. The measurement system is calibrated carefully before loading into the thermal evaporator. This in-situ thermoelectricity measurement system has been thoroughly tested on various materials, including Bi, Te, and Bi$_2$Te$_3$, at high temperatures up to 500 K

Physical Layer Security: Detection of Active Eavesdropping Attacks by Support Vector Machines

This paper presents a framework for converting wireless signals into structured datasets, which can be fed into machine learning algorithms for the detection of active eavesdropping attacks at the physical layer. More specifically, a wireless communication system, which consists of K legal users, one access point (AP) and one active eavesdropper, is considered. To cope with the eavesdropper who breaks into the system during the uplink phase, we first build structured datasets based on several different features. We then apply support vector machine (SVM) classifiers and one-class SVM classifiers to those structured datasets for detecting the presence of eavesdropper. Regarding the data, we first process received signals at the AP and then define three different features (i.e., MEAN, RATIO and SUM) based on the post-processing signals. Noticeably, our three defined features are formulated such that they have relevant statistical properties. Enabling the AP to simulate the entire process of transmission, we form the so-called artificial training data (ATD) that is used for training SVM (or one-class SVM) models. While SVM is preferred in the case of having perfect channel state information (CSI) of all channels, one-class SVM is preferred in the case of having only the CSI of legal users. We also evaluate the accuracy of the trained models in relation to the choice of kernel functions, the choice of features, and the change of eavesdropper's power. Numerical results show that the accuracy is relatively sensitive to adjusting parameters. Under some settings, SVM classifiers (or even one-class SVM) can bring about the accuracy of over 90%.Comment: All versions on this site are withdrawn because of their serious mistakes. Moreover, the contributions of the co-authors were not considered carefully. Two co-authors have little contributions, which cannot constitute any main contribution. It was a mistake when the first author forgot to update the actual authors, and he hurried to upload the incomplete and flaw file

Using Joint Utilities of the Times to Response and Toxicity to Adaptively Optimize Schedule–Dose Regimes

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/101836/1/biom12065-sm-0001-SuppData.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/101836/2/biom12065.pd